Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the topics of all words in the same docume...

The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and...

I n t r o d u c t i o n Computational analysis is increasingly important for inferring the functions and structures of proteins [1] because the speed of D N A sequencing has long since surpassed the rate at which the biological function of sequences can be elucidated experimentally. Established sequence comparison algorithms detect significant similarities between known database sequences and 3...

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the tra...

Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic models that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that oper...

This paper describes the AT&T WATSON real-time speech recognizer, the product of several decades of research at AT&T. The recognizer handles a wide range of vocabulary sizes and is based on continuous-density hidden Markov models for acoustic modeling and finite state networks for language modeling. The recognition network is optimized for efficient search. We identify the algorithms used for h...

In this paper, we describe a system for the recognition of musical instruments from isolated notes or drum samples. We first describe a baseline system that uses uses melfrequency cepstral coefficients and their first derivatives as features, and continuous-density hidden Markov models (HMMs). Two improvements are proposed to increase the performance of this baseline system. First, transforming...

It is well known that speaker identification yields very high performance in a neutral talking environment; on the other hand, the performance has been sharply declined in a shouted talking environment. This work aims at proposing, implementing, and evaluating novel Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s) to improve the low performance of text-independent speaker ide...

Within the context of robust acoustic features for automatic speech recognition (ASR), we evaluated mel-frequency cepstral coefficients (MFCCs) derived from two spectral representation techniques, i.e. the fast Fourier transform (FFT) and linear pre­ dictive coding (LPC). ASR systems based on the two feature types were tested on a digit recognition task using continuous density hidden Markov ph...

We present a simplified derivation of the extended Baum-Welch procedure, which shows that it can be used for Maximum Mutual Information (MMI) of a large class of continuous emission density hidden Markov models (HMMs). We use the extended Baum-Welch procedure for discriminative estimation of MLLR-type speaker adaptation transformations. The resulting adaptation procedure, termed Conditional Max...